1. Field
Exemplary embodiments of the present invention relate generally to egomotion technology, and, more particularly, to an egomotion estimation system and method employing a stereo camera mounted on a moving object, such as, for example, a vehicle.
2. Description of the Related Art
Egomotion refers to the three-dimensional (3D) motion of a camera. Egomotion estimation refers to an operation for estimating egomotion based on a series of images obtained by the camera.
Egomotion estimation is important for understanding and reconfiguring 3D scenes including computer vision systems for the operation of moving objects, such as moving vehicles, self-guided robots, and so forth.
For understanding and reconfiguring 3D scenes, computer vision technologies which segment image frames of a scene may be employed. However, it is difficult to segment image frames captured with a moving camera. Taking into consideration the movement of the camera, a preliminary egomotion estimation may be performed.
According to a conventional egomotion estimation, feature points are recognized in a series of image frames, tracked and compared. This method is generally too cumbersome and require systems with large calculation capacity.
According to another conventional egomotion estimation, egomotion is estimated based on land marks found within a single image frame, such as lane marking or texts on a road surface. In many cases, however, it is hard to acquire clearly defined land marks on an actual road surface.
According to yet another conventional egomotion estimation, egomotion is estimated by acquiring an image through a single moving camera, extracting feature points from the acquired image, applying an optical flow to the extracted feature points to estimate the motion vectors of the extracted feature points between frames, and applying random sample consensus (RANSAC) to the estimated motion vectors. However, many incorrect feature points may be extracted from a road surface and motion vectors based on pixel values may increase the likelihood for errors.